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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Updated: Dec 6, 2025

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DockCoV2: a drug database against SARS-CoV-2.

Ting-Fu Chen1, Yu-Chuan Chang1, Yi Hsiao1

  • 1Taiwan AI Labs, Taipei 10351, Taiwan.

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|October 9, 2020
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Summary

DockCoV2 is a new drug database designed to accelerate the discovery of potential therapeutics for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It predicts drug-protein binding affinities for 3,109 drugs against key viral proteins.

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Area of Science:

  • Virology and Drug Discovery
  • Computational Biology and Bioinformatics

Background:

  • The COVID-19 pandemic presents a global health crisis, necessitating rapid identification of antiviral strategies.
  • Targeting essential viral enzymes is a key approach to inhibiting SARS-CoV-2 replication.
  • SARS-CoV-2 utilizes specific host cell proteins like ACE2 and TMPRSS2 for viral entry and replication.

Purpose of the Study:

  • To develop DockCoV2, a comprehensive drug database for SARS-CoV-2.
  • To predict the binding affinity of approved drugs against critical SARS-CoV-2 proteins.
  • To facilitate accelerated drug discovery for COVID-19 therapeutics.

Main Methods:

  • Development of the DockCoV2 database containing 3,109 drugs.
  • Prediction of binding affinities between drugs and seven key SARS-CoV-2 proteins.
  • Integration of drug-protein docking data with external database cross-linking.

Main Results:

  • DockCoV2 provides state-of-the-art prediction results for drug-protein interactions.
  • The database includes FDA-approved and Taiwan NHI drugs.
  • Users can download docking data and access experimental information on MERS and SARS-CoV drug efficacy.

Conclusions:

  • DockCoV2 serves as a valuable, user-friendly resource for identifying potential SARS-CoV-2 drug candidates.
  • The database aids researchers in exploring existing drugs for repurposing against COVID-19.
  • DockCoV2 streamlines the drug discovery process by consolidating prediction and experimental data.